{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET = 'pattern_trend10'\n",
    "ORIG = 'trend'\n",
    "FILE = 'trend10'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def abs_normalize(a):\n",
    "    a = a / np.maximum(np.absolute(a.max(axis=0)), np.absolute(a.min(axis=0)))\n",
    "    a = np.nan_to_num(a)\n",
    "    return (a / 2 + 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.conda/envs/py3.9test/lib/python3.9/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1)\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "def scale_data(train, test, validation):\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1), clip=True).fit(train)\n",
    "\n",
    "    train_scaled = scaler.transform(train)\n",
    "    test_scaled = scaler.transform(test)\n",
    "    validation_scaled = scaler.transform(validation)\n",
    "\n",
    "    return train_scaled, test_scaled, validation_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_0</th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "      <th>col_3</th>\n",
       "      <th>col_4</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.131232</td>\n",
       "      <td>2.607850</td>\n",
       "      <td>0.066011</td>\n",
       "      <td>4.381993</td>\n",
       "      <td>-1.894449</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.398736</td>\n",
       "      <td>2.403965</td>\n",
       "      <td>0.442701</td>\n",
       "      <td>4.554839</td>\n",
       "      <td>-1.530790</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.809108</td>\n",
       "      <td>2.170690</td>\n",
       "      <td>0.702547</td>\n",
       "      <td>3.998227</td>\n",
       "      <td>-1.414847</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.007888</td>\n",
       "      <td>1.874675</td>\n",
       "      <td>1.006700</td>\n",
       "      <td>3.990737</td>\n",
       "      <td>-0.910490</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.340091</td>\n",
       "      <td>1.311106</td>\n",
       "      <td>1.337947</td>\n",
       "      <td>3.413961</td>\n",
       "      <td>-0.720344</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      col_0     col_1     col_2     col_3     col_4  anomaly\n",
       "0 -0.131232  2.607850  0.066011  4.381993 -1.894449        0\n",
       "1  0.398736  2.403965  0.442701  4.554839 -1.530790        0\n",
       "2  0.809108  2.170690  0.702547  3.998227 -1.414847        0\n",
       "3  1.007888  1.874675  1.006700  3.990737 -0.910490        0\n",
       "4  1.340091  1.311106  1.337947  3.413961 -0.720344        0"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series = pd.read_csv(f'{FILE}_train_test_val.csv')\n",
    "time_series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_0</th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "      <th>col_3</th>\n",
       "      <th>col_4</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.131232</td>\n",
       "      <td>2.607850</td>\n",
       "      <td>0.066011</td>\n",
       "      <td>4.381993</td>\n",
       "      <td>-1.894449</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.398736</td>\n",
       "      <td>2.403965</td>\n",
       "      <td>0.442701</td>\n",
       "      <td>4.554839</td>\n",
       "      <td>-1.530790</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.809108</td>\n",
       "      <td>2.170690</td>\n",
       "      <td>0.702547</td>\n",
       "      <td>3.998227</td>\n",
       "      <td>-1.414847</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.007888</td>\n",
       "      <td>1.874675</td>\n",
       "      <td>1.006700</td>\n",
       "      <td>3.990737</td>\n",
       "      <td>-0.910490</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.340091</td>\n",
       "      <td>1.311106</td>\n",
       "      <td>1.337947</td>\n",
       "      <td>3.413961</td>\n",
       "      <td>-0.720344</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      col_0     col_1     col_2     col_3     col_4  anomaly\n",
       "0 -0.131232  2.607850  0.066011  4.381993 -1.894449        0\n",
       "1  0.398736  2.403965  0.442701  4.554839 -1.530790        0\n",
       "2  0.809108  2.170690  0.702547  3.998227 -1.414847        0\n",
       "3  1.007888  1.874675  1.006700  3.990737 -0.910490        0\n",
       "4  1.340091  1.311106  1.337947  3.413961 -0.720344        0"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_time_series = time_series[:20000]\n",
    "train_time_series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-15.097620609417833\n",
      "85.04304598705383\n"
     ]
    }
   ],
   "source": [
    "print(train_time_series['col_4'].min())\n",
    "print(train_time_series['col_4'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_0</th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "      <th>col_3</th>\n",
       "      <th>col_4</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20000</th>\n",
       "      <td>0.113480</td>\n",
       "      <td>2.541960</td>\n",
       "      <td>0.002418</td>\n",
       "      <td>4.413778</td>\n",
       "      <td>2.513933</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20001</th>\n",
       "      <td>0.425330</td>\n",
       "      <td>2.352705</td>\n",
       "      <td>0.402986</td>\n",
       "      <td>4.343363</td>\n",
       "      <td>2.861954</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20002</th>\n",
       "      <td>0.593477</td>\n",
       "      <td>2.385319</td>\n",
       "      <td>0.795860</td>\n",
       "      <td>4.250515</td>\n",
       "      <td>3.343678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20003</th>\n",
       "      <td>0.990708</td>\n",
       "      <td>1.682974</td>\n",
       "      <td>1.020078</td>\n",
       "      <td>3.947763</td>\n",
       "      <td>3.418243</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20004</th>\n",
       "      <td>1.328372</td>\n",
       "      <td>1.452374</td>\n",
       "      <td>1.258230</td>\n",
       "      <td>3.126827</td>\n",
       "      <td>3.671927</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          col_0     col_1     col_2     col_3     col_4  anomaly\n",
       "20000  0.113480  2.541960  0.002418  4.413778  2.513933        0\n",
       "20001  0.425330  2.352705  0.402986  4.343363  2.861954        0\n",
       "20002  0.593477  2.385319  0.795860  4.250515  3.343678        0\n",
       "20003  0.990708  1.682974  1.020078  3.947763  3.418243        0\n",
       "20004  1.328372  1.452374  1.258230  3.126827  3.671927        0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_time_series = time_series[20000:40000]\n",
    "test_time_series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>col_0</th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "      <th>col_3</th>\n",
       "      <th>col_4</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40000</th>\n",
       "      <td>-0.094802</td>\n",
       "      <td>2.652171</td>\n",
       "      <td>0.018939</td>\n",
       "      <td>4.550506</td>\n",
       "      <td>-6.491240</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40001</th>\n",
       "      <td>0.359750</td>\n",
       "      <td>2.256046</td>\n",
       "      <td>0.331234</td>\n",
       "      <td>4.466656</td>\n",
       "      <td>-6.159894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40002</th>\n",
       "      <td>0.823503</td>\n",
       "      <td>2.183344</td>\n",
       "      <td>0.822009</td>\n",
       "      <td>4.316569</td>\n",
       "      <td>-5.682100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40003</th>\n",
       "      <td>1.080265</td>\n",
       "      <td>1.816790</td>\n",
       "      <td>1.056659</td>\n",
       "      <td>3.858038</td>\n",
       "      <td>-5.463839</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40004</th>\n",
       "      <td>1.248927</td>\n",
       "      <td>1.374257</td>\n",
       "      <td>1.238567</td>\n",
       "      <td>3.136138</td>\n",
       "      <td>-5.198222</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          col_0     col_1     col_2     col_3     col_4  anomaly\n",
       "40000 -0.094802  2.652171  0.018939  4.550506 -6.491240        0\n",
       "40001  0.359750  2.256046  0.331234  4.466656 -6.159894        0\n",
       "40002  0.823503  2.183344  0.822009  4.316569 -5.682100        0\n",
       "40003  1.080265  1.816790  1.056659  3.858038 -5.463839        0\n",
       "40004  1.248927  1.374257  1.238567  3.136138 -5.198222        0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_time_series = time_series[40000:]\n",
    "val_time_series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = train_time_series.anomaly.to_numpy()\n",
    "test_labels = test_time_series.anomaly.to_numpy()\n",
    "val_labels = val_time_series.anomaly.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train  (20000, 5)\n",
      "Test:  (20000, 5)\n",
      "Val:  (10000, 5)\n"
     ]
    }
   ],
   "source": [
    "train_no_labels = train_time_series.drop(['anomaly'], axis=1).to_numpy()\n",
    "test_no_labels = test_time_series.drop(['anomaly'], axis=1).to_numpy()\n",
    "val_no_labels = val_time_series.drop(['anomaly'], axis=1).to_numpy()\n",
    "print('Train ', train_no_labels.shape)\n",
    "print('Test: ', test_no_labels.shape)\n",
    "print('Val: ', val_no_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train ratio:  1.01\n",
      "Val ratio:  0.8999999999999999\n"
     ]
    }
   ],
   "source": [
    "train_ratio = (train_labels.sum() / train_labels.shape[0]) * 100\n",
    "val_ratio = (val_labels.sum() / val_labels.shape[0]) * 100\n",
    "print('Train ratio: ', train_ratio)\n",
    "print('Val ratio: ', val_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalized dataset\n"
     ]
    }
   ],
   "source": [
    "if ORIG != 'pattern_trend':    \n",
    "    train_normalized = abs_normalize(train_no_labels)\n",
    "    val_normalized = abs_normalize(val_no_labels)\n",
    "    test_normalized = abs_normalize(test_no_labels)\n",
    "    print('normalized dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def expand_labels(data, labels):\n",
    "    labels_reshaped = np.zeros_like(data)\n",
    "    for idx in range(0, len(labels)):\n",
    "        if labels[idx]:\n",
    "            labels_reshaped[idx][0:labels_reshaped.shape[1]] = 1\n",
    "    return labels_reshaped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# val_labels_reshaped = expand_labels(val_normalized, val_labels)\n",
    "# test_labels_reshaped = expand_labels(test_normalized, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FOR RATIO ABLATION\n",
    "#test5 = np.load(f'../processed/{ORIG}/test.npy')\n",
    "#labels5 = np.load(f'../processed/{ORIG}/labels.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "def scale_test(test):\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1)).fit(test)\n",
    "\n",
    "    #train_scaled = scaler.transform(train)\n",
    "    test_scaled = scaler.transform(test)\n",
    "\n",
    "    return test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "trend5_series = pd.read_csv('trend_train_test_val.csv')\n",
    "test_trend5 = trend5_series[20000:40000]\n",
    "#labels5 = test_trend5.anomaly.to_numpy()\n",
    "#test_trend5 = test_trend5.drop(['anomaly'], axis=1).to_numpy()\n",
    "#labels5 = expand_labels(test_trend5, labels5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-97.97744390787436\n",
      "-1.0244058283630473\n"
     ]
    }
   ],
   "source": [
    "print(test_trend5['col_4'].min())\n",
    "print(test_trend5['col_4'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ratio = 1\n",
    "#ds = f'pattern_trend{ratio}_test5'\n",
    "#test5_normalized = scale_test(train_no_labels, test_trend5)\n",
    "test5_normalized = scale_test(test_trend5)\n",
    "#np.save(f'../processed/{ds}/test.npy', normalized_test_for_ds)\n",
    "#np.save(f'../processed/{ds}/labels.npy', labels_5_expanded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalized trend\n"
     ]
    }
   ],
   "source": [
    "# if ORIG == 'pattern_trend':\n",
    "#     train_normalized, test5_normalized, val_normalized = scale_data(train_no_labels, test5, val_no_labels)\n",
    "#     print('normalized trend')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_labels_reshaped = expand_labels(val_normalized, val_labels)\n",
    "#test_labels_reshaped = expand_labels(test_normalized, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved trend\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "os.makedirs(f'../processed/{DATASET}_test5', exist_ok=True)\n",
    "# np.save(f'../processed/{DATASET}_test5/train.npy', train_normalized)\n",
    "if ORIG != 'pattern_trend' and ORIG != 'trend':\n",
    "    np.save(f'../processed/{DATASET}_test5/test.npy', test5)\n",
    "    print('saved NOT trend')\n",
    "else:\n",
    "    np.save(f'../processed/{DATASET}_test5/test.npy', test5_normalized)\n",
    "    print('saved trend')\n",
    "np.save(f'../processed/{DATASET}_test5/labels.npy', labels5)\n",
    "# np.save(f'../processed/{DATASET}_test5/validation.npy', val_normalized)\n",
    "# np.save(f'../processed/{DATASET}_test5/labels_validation.npy', val_labels_reshaped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 5)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_5_reshaped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "train = np.load('../processed/pattern_trend15_test5/train.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa4cef2c520>]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(train[:, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa4cee9a460>]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(train[:, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(f'../processed/{DATASET}_test5/labels.npy', labels5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3.9test",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
